@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
112 lines • 4.5 kB
JavaScript
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import { env } from './environment';
import * as util from './util';
export class Profiler {
constructor(backendTimer, logger) {
this.backendTimer = backendTimer;
this.logger = logger;
if (logger == null) {
this.logger = new Logger();
}
}
profileKernel(kernelName, inputs, f) {
let outputs;
const holdResultWrapperFn = () => {
outputs = f();
};
let timer;
const start = util.now();
if (this.backendTimer.timerAvailable()) {
timer = this.backendTimer.time(holdResultWrapperFn);
}
else {
holdResultWrapperFn();
for (const output of outputs) {
output.dataSync();
}
timer = Promise.resolve({ kernelMs: util.now() - start });
}
if (env().getBool('CHECK_COMPUTATION_FOR_ERRORS')) {
for (let i = 0; i < outputs.length; i++) {
const output = outputs[i];
// Dangling promise here because we don't want to propagate up
// asynchronicity.
output.data().then(tensorVals => {
checkComputationForErrors(tensorVals, output.dtype, kernelName);
});
}
}
const kernelProfile = {
kernelName,
outputs,
inputs,
timeMs: timer.then(timing => timing.kernelMs),
extraInfo: timer.then(timing => timing.getExtraProfileInfo != null ?
timing.getExtraProfileInfo() :
'')
};
return kernelProfile;
}
logKernelProfile(kernelProfile) {
const { kernelName, outputs, timeMs, inputs, extraInfo } = kernelProfile;
outputs.forEach(result => {
Promise.all([result.data(), timeMs, extraInfo]).then(valueContainer => {
this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]);
});
});
}
}
export function checkComputationForErrors(vals, dtype, kernelName) {
if (dtype !== 'float32') {
// Only floating point computations will generate NaN values
return false;
}
for (let i = 0; i < vals.length; i++) {
const num = vals[i];
if (isNaN(num) || !isFinite(num)) {
// Throwing custom exception so behavior is testable.
console.warn(`Found ${num} in the result of '${kernelName}'`);
return true;
}
}
return false;
}
export class Logger {
logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {
const time = typeof timeMs === 'number' ? util.rightPad(`${timeMs}ms`, 9) :
timeMs['error'];
const paddedName = util.rightPad(name, 25);
const rank = result.rank;
const size = result.size;
const shape = util.rightPad(result.shape.toString(), 14);
let inputShapesDescription = '';
for (const name in inputs) {
const input = inputs[name];
if (input != null) {
// The input might be a non-tensor (e.g HTMLImageElement), in which case
// we claim the output shape as input shape.
const inputShape = input.shape || result.shape;
const inputRank = inputShape.length;
inputShapesDescription +=
`${name}: ${inputRank}D ${inputRank > 0 ? inputShape : ''} `;
}
}
console.log(`%c${paddedName}\t%c${time}\t%c${rank}D ${shape}\t%c${size}\t%c${inputShapesDescription}\t%c${extraInfo}`, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue');
}
}
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